Table 2 The list of notations

w_t(i)

weight of the i^th training example at the t^th iteration

H(x)

final strong classifier

acc

accuracy of classification

c(y_i,y_j)

tcost function, which assigns a cost to the event of predicting class y_j when the true class is y_i

N

total number of samples or instances in the dataset

p

number of negative samples

L(x,y)

real loss associated with a prediction for a given class y when the input is x

τ

index or identifier for the weak learners in the ensemble that the AdaBoost algorithm generates

α_τ

weight assigned to the τ-th classifier in the ensemble

h_τ(x)

hypothesis or prediction made by the τ-th classifier for the sample x

L

set of all possible states in the environment

A

a set of all possible actions that the agent can take in a given state

R

reward received after transitioning from one state to another due to an action taken by the agent

P

probability of transitioning from one state to another

H

a dataset or a set of data points that encapsulate the historical movement of the end-device

l_i

i^th position of the device in a sequence of positions

t_i

arrival time of the device corresponding to i^th position

w_ij

weight of the visit from l_i to l_j

h_i

i^th basic classifier

P_ij

transition probability from l_i to l_j, i ≠ j

α

learning rate

γ

discount factor

m

the total number of the training data

t

current iteration or round of the boosting process

Q(s,a)

expected cumulative reward for taking action a in state s

Q′ (s′,a′)

estimated maximum reward for the next state s′ over all possible actions a′.

R(s,a)

reward received after taking action a in state s